TY  - GEN
AV  - public
N2  - In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal platform revenue and user experience. Consequently, major e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible ways to display ads. In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased? More specifically, we consider two types of constraints: request-level constraint ensures user experience for each user visit, and platform-level constraint controls the overall platform monetization rate. We model this problem as a Constrained Markov Decision Process with per-state constraint (psCMDP) and propose a constrained two-level reinforcement learning approach to decompose the original problem into two relatively independent sub-problems. To accelerate policy learning, we also devise a constrained hindsight experience replay mechanism. Experimental evaluations on industry-scale real-world datasets demonstrate the merits of our approach in both obtaining higher revenue under the constraints and the effectiveness of the constrained hindsight experience replay mechanism.
EP  - 2603
A1  - Wang, W
A1  - Jin, J
A1  - Hao, J
A1  - Chen, C
A1  - Yu, C
A1  - Zhang, W
A1  - Wang, J
A1  - Hao, X
A1  - Wang, Y
A1  - Li, H
A1  - Xu, J
A1  - Gai, K
UR  - https://doi.org/10.1145/3357384.3357806
CY  - New York, NY, USA
ID  - discovery10116272
SP  - 2595
PB  - Association for Computing Machinery (ACM)
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
Y1  - 2019/11/03/
T3  - ACM International Conference on Information and Knowledge Management (CIKM)
TI  - Learning Adaptive Display Exposure for Real-Time Advertising
ER  -